Abstract:

Background: Determining the underlying etiology of dementia can be challenging. Computer-
based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison
of data and assist clinicians.

Objectives: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of
dementia in memory clinics.

Methods: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive
decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed
them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological
tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND
tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic
groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal
dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and
confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up
diagnosis was used as the reference diagnosis.

Results: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological
diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy
did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS,
0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%,
p=0.0011).

Conclusion: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased
clinicians’ confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential
diagnosis of dementia.

Abstract:Background: Determining the underlying etiology of dementia can be challenging. Computer-
based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison
of data and assist clinicians.

Objectives: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of
dementia in memory clinics.

Methods: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive
decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed
them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological
tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND
tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic
groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal
dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and
confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up
diagnosis was used as the reference diagnosis.

Results: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological
diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy
did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS,
0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%,
p=0.0011).

Conclusion: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased
clinicians’ confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential
diagnosis of dementia.